Future and Emerging Trends in Language Technology. Machine Learning and Big Data: Second International Workshop, FETLT 2016, Seville, Spain, November 30 -December 2, 2016, Revised Selected Papers

by José F Quesada (Editor), Francisco-Jesús Martín Mateos (Editor), Teresa López Soto (Editor)

Overview

This book constitutes revised selected papers from the Second International Workshop on Future and Emerging Trends in Language Technology, FETLT 2016, which took place in Seville, Spain, in November 2016.
The 10 full papers and 5 position papers presented in this volume were carefully reviewed and selected from 18 submissions. In 2016 the conference focused on Machine Learning and Big Data.

Product Details

ISBN-13:	9783319693644
Publisher:	Springer International Publishing
Publication date:	12/03/2017
Series:	Lecture Notes in Computer Science , #10341
Edition description:	1st ed. 2017
Pages:	199
Product dimensions:	6.10(w) x 9.25(h) x (d)

Table of Contents

Position Papers.- With or without Meaning? Hype Cycles in Language Technology and what We Can Learn from Them.- Observatory for Language Resources and Machine Translation in Europe - LT Observatory.- The Rise of the Conversational Interface: A new Kid on the Block.- Spanish Language Technologies Plan.- Improving Collaboration between the European Language Technology Industry & Research: A new Framework for Supply & Demand.- Contributed Papers.- LifeLine dialogues with Roberta.- An affective utility model of user motivation for counselling dialogue systems.- Exploring flexibility in natural language generation throughout discursive analysis of new textual genres.- Bootstrapping technique + embeddings = Emotional corpus annotated automatically.- Rapid construction of a web-enabled medical speech to sign language translator using recorded video.- Incorporating syllable phonotactics to improve grapheme to phoneme translation.- Hybrid conceptual and statistical measure for semantic textualsimilarity evaluation.- General representation model for text similarity.- Extending feature decay algorithms using alignment entropy.- Towards a topic discovery and tracking system with application to news items.